scholarly journals Genomic variance of the 2019‐nCoV coronavirus

2020 ◽  
Vol 92 (5) ◽  
pp. 522-528 ◽  
Author(s):  
Carmine Ceraolo ◽  
Federico M. Giorgi
Keyword(s):  
Genomic Variance

scholarly journals Novel Genomic Regions Associated with Intramuscular Fatty Acid Composition in Rabbits

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2090
Author(s):  
Houda Laghouaouta ◽  
Bolívar Samuel Sosa-Madrid ◽  
Agostina Zubiri-Gaitán ◽  
Pilar Hernández ◽  
Agustín Blasco
Keyword(s):  
Fatty Acids ◽  
Fatty Acid Composition ◽  
Fatty Acid ◽  
Acid Composition ◽  
Correlated Response ◽  
A Genome ◽  
Genomic Variance ◽  
Genomic Regions ◽  
Imf Content ◽  
The Imf

Intramuscular fat (IMF) content and its composition affect the quality of meat. Selection for IMF generated a correlated response on its fatty acid composition. The increase of IMF content is associated with an increase of its saturated (SFA) and monounsaturated (MUFA) fatty acids, and consequently a decrease of polyunsaturated fatty acids (PUFA). We carried out a genome wide association study (GWAS) for IMF composition on two rabbit lines divergently selected for IMF content, using a Bayes B procedure. Association analyses were performed using 475 individuals and 90,235 Single Nucleotide Polymorphisms (SNPs). The main objectives were to identify genomic regions associated with the IMF composition and to generate a list of candidate genes. Genomic regions associated with the intramuscular fatty acid composition were spread across different rabbit chromosomes (OCU). An important region at 34.0–37.9 Mb on OCU1 was associated with C14:0, C16:0, SFA, and C18:2n6, explaining 3.5%, 11.2%, 11.3%, and 3.2% of the genomic variance, respectively. Another relevant genomic region was found to be associated at 46.0–48.9 Mb on OCU18, explaining up to 8% of the genomic variance of MUFA/SFA. The associated regions harbor several genes related to lipid metabolism, such as SCD, PLIN2, and ERLIN1. The main genomic regions associated with the fatty acids were not previously associated with IMF content in rabbits. Nonetheless, MTMR2 is the only gene that was associated with both the IMF content and composition in rabbits. Our study highlighted the polygenic nature of the fatty acids in rabbits and elucidated its genetic background.

scholarly journals Best Prediction of the Additive Genomic Variance in Random-Effects Models

Genetics ◽  
2019 ◽  
Vol 213 (2) ◽  
pp. 379-394 ◽  
Author(s):  
Nicholas Schreck ◽  
Hans-Peter Piepho ◽  
Martin Schlather
Keyword(s):  
Random Effects ◽  
Random Effects Models ◽  
Genomic Variance

scholarly journals Best Prediction of the Additive Genomic Variance in Random-Effects Models

2018 ◽  
Author(s):  
Nicholas Schreck ◽  
Hans-Peter Piepho ◽  
Martin Schlather
Keyword(s):  
Linkage Disequilibrium ◽  
Random Effects ◽  
Linear Models ◽  
Additive Genetic Variance ◽  
Random Variable ◽  
Phenotypic Data ◽  
Random Effects Models ◽  
Additional Information ◽  
Genomic Marker ◽  
Genomic Variance

ABSTRACTThe additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium.We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the gBLUP-method. The resulting best predictor is the conditional (or posterior) expectation of the additive genomic variance when using the additional information given by the phenotypic data, and is structurally in accordance with the genomic equivalent of the classical additive genetic variance in random-effects models. In particular, the best predictor includes the contribution of (marker) linkage disequilibrium to the additive genomic variance and eliminates the missing contribution of LD that is caused by the assumptions of statistical frameworks such as the random-effects model. We derive an empirical best predictor (eBP) and compare its performance with common approaches to estimate the additive genomic variance in random-effects models on commonly used genomic datasets.

scholarly journals Mutations of SARS-CoV-2 nsp14 exhibit strong association with increased genome-wide mutation load

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10181 ◽  
Author(s):  
Doğa Eskier ◽  
Aslı Suner ◽  
Yavuz Oktay ◽  
Gökhan Karakülah
Keyword(s):  
Rna Synthesis ◽  
Selective Pressure ◽  
Strong Association ◽  
Disease Spread ◽  
Replication Proteins ◽  
Mutation Load ◽  
Global Impact ◽  
Genome Wide ◽  
Genomic Variance ◽  
Viral Isolates

SARS-CoV-2 is a betacoronavirus responsible for COVID-19, a pandemic with global impact that first emerged in late 2019. Since then, the viral genome has shown considerable variance as the disease spread across the world, in part due to the zoonotic origins of the virus and the human host adaptation process. As a virus with an RNA genome that codes for its own genomic replication proteins, mutations in these proteins can significantly impact the variance rate of the genome, affecting both the survival and infection rate of the virus, and attempts at combating the disease. In this study, we analyzed the mutation densities of viral isolates carrying frequently observed mutations for four proteins in the RNA synthesis complex over time in comparison to wildtype isolates. Our observations suggest mutations in nsp14, an error-correcting exonuclease protein, have the strongest association with increased mutation load without selective pressure and across the genome, compared to nsp7, nsp8 and nsp12, which form the core polymerase complex. We propose nsp14 as a priority research target for understanding genomic variance rate in SARS-CoV-2 isolates and nsp14 mutations as potential predictors for high mutability strains.

Decomposing genomic variance using information from GWA, GWE and eQTL analysis

2015 ◽  
Vol 47 (2) ◽  
pp. 165-173 ◽  
Author(s):  
A. Ehsani ◽  
L. Janss ◽  
D. Pomp ◽  
P. Sørensen
Keyword(s):  
Eqtl Analysis ◽  
Genomic Variance

scholarly journals Transcriptomic basis for salt tolerance and disease resistance of silverleaf sunflower revealed by Iso-seq and RNA-seq

2019 ◽  
Author(s):  
Jing Bing ◽  
Yunhe Ling ◽  
Peipei An ◽  
Enshi Xiao ◽  
Chunlian Li ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Salt Tolerance ◽  
Reference Genome ◽  
Transcriptome Assembly ◽  
Future Research ◽  
Rna Seq ◽  
Gene Expressions ◽  
Breeding Programs ◽  
Genomic Variance ◽  
Cultivated Species

Abstract Background Silverleaf sunflower, Helianthus argophyllus , is one of the most important wild species that have been usually used for the improvement of cultivated sunflower. Although a reference genome is now available for the cultivated species, H. annuus , its effect in helping understanding the mechanisms underlying the traits of H. argophyllus is limited by the substantial genomic variance between these two species.Results In this study, we generated a high-quality reference transcriptome of H. argophyllus using Iso-seq strategy. This assembly contains 50,153 unique genes covering more than 91% of the whole genes. Among them, we find 205 genes that are absent in the cultivated species and 475 fusion genes containing components of coding or non-coding sequences from the genome of H. annuus . It is interesting that in line with the strong disease resistance observed for H. argophyllus , these H. argophyllus -specific genes are predominantly related to functions of resistance. We have also profiled the gene expressions in leaf and root under normal or salt stressed conditions and, as a result, find distinct transcriptomic responses to salt stress in leaf and root. Particularly, genes involved in several critical processes including the synthesis and metabolism of glutamate and carbohydrate transport are reversely regulated in leaf and root.Conclusions Overall, this study provided insights into the genomic mechanisms underlying the disease resistance and salt tolerance of silverleaf sunflower and the transcriptome assembly and the genes identified in this study can serve as a complement data resources for future research and breeding programs of sunflowers.

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